Deep Learning-based Basketball Free Throw Attitude Analysis and Hit Probability Prediction System Research

Abstract

Abstract Traditional basketball free-throw hitting probability prediction mainly relies on naked eye vision to analyze basketball players, which makes it difficult to achieve stable and accurate free-throw hitting probability prediction. In this study, we propose a deep learning-based algorithm for recognizing basketball free throw poses and predicting the probability of hitting free throws, and we build a corresponding system framework. DetectNet is used to screen free throw motion detectors, real-time estimate and recognition of free throw poses, and code rewriting. At the same time, Open Pose is used as a human skeletal joint point detector to extract the joint point information of the free throw shooter. The joint points with wrong information are repaired, and the classification prediction of the free throw hitting result is realized based on the support vector machine. In the performance simulation experiments, the average accuracy of the free throw pose recognition method proposed in this paper is as high as 98.55% on the MSR Action 3D dataset, and the recognition rate of the elbow lift pose, which is the most difficult to recognize, is also higher than that of other comparative algorithms. The hit probability prediction method is also 12.64% and 6.25% more accurate than the OpenPose algorithm in hit-and-miss prediction, and the accuracy has also improved by 4.47% and 5.41%, respectively. The free throw pose recognition and hit probability prediction method in this paper has excellent performance.